Description

Pathifier is an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample.

Arguments

data

The n x m mRNA expression matrix, where n is the number of genes and m the number of samples.

allgenes

A list of n identifiers of genes.

syms

A list of p pathways, each pathway is a list of the genes it contains (as appear in "allgenes").

pathwaynames

The names of the p pathways.

normals

A list of m logicals, true if a normal sample, false if tumor.

ranks

External knowledge on the ranking of the m samples, if exists (to use initial guess)

attempts

Number of runs to determine stability.

maximize_stability

If true, throw away components leading to low stability of sampling noise.

logfile

Name of the file the log should be written to (use stdout if empty).

samplings

A matrix specifying the samples that should be chosen in each sampling attempt, chooses a random matrix if samplings is NULL.

min_exp

The minimal expression considered as a real signal. Any values below are thresholded to be min_exp.

min_std

The minimal allowed standard deviation of each gene. Genes with lower standard deviation are divided by min_std instead of their actual standard deviation. (Recommended: set min_std to be the technical noise).